I'm also an independent researcher, and I just wanted to say it's exciting to see other individuals making real contributions! One thing I've noticed is that as I'm discovering some very deep stuff, the imposter syndrome is hitting me hard because I don't have a research group to vibe off of. I have scientific training and 17 years of ML experience, but I think it's still natural to question yourself when you're pushing past the SOTA and finding deep patterns that the field has missed.

If it's useful to you, I'm happy to be a sounding board/vibes partner for your research. My contact info is in my profile.

Very good find, thank you for writing it down. For some time I had the impression that they could be unified, I just never bothered trying.

From the paper:

Structured State Space Models and Mamba. Models like Mamba [Gu and Dao, 2023] can be in- terpreted within GWO as employing a sophisticated Path, Shape, and Weight. The Path is defined by a structured state-space recurrence, enabling it to model long-range dependencies efficiently. The Shape is causal (1D), processing information sequentially. Critically, the Weight function is highly dynamic and input- dependent, realized through selective state parameters that allow the model to focus on or forget information based on the context, creating an effective content-aware bottleneck for sequences.

That's a fantastic question, and you've hit on a perfect example of the GWO framework in action. The key difference is the level of abstraction: GWO is a general grammar to describe and design operations, while Mamba is a specific, highly-engineered model that can be described by that grammar. In fact, as I mention in the paper, we can analyze Mamba using the (P, S, W) components: Path (P): A structured state-space recurrence. This is a very sophisticated path designed to efficiently handle extremely long-range dependencies, unlike a simple sliding window or a dense global matrix. Shape (S): It's causal and 1D. It processes information sequentially, respecting the nature of time-series or language data. Weight (W): This is Mamba's superpower. The weights are highly dynamic and input-dependent, controlled by its selective state parameters. This creates an incredibly efficient, content-aware information bottleneck, allowing the model to decide what to remember and what to forget based on the context. So, Mamba isn't a competitor to the GWO theory; it's a stellar example of it. It's a brilliant instance of "Structural Alignment" where the (P, S, W) configuration is perfectly tailored for the structure of sequential data. Thanks for asking this, it's a great point for discussion.